The substantial structures of Deep Learning (DL) models have been proved to be very effective for several discriminating tasks related to Computer Vision applications such as prediction, image segmentation, or object recognition with the help of progressive and efficient computational power, and in-depth network architecture [1]}. However, DL networks can rarely perform well without the availability of a massive dataset. This limitation proves to be even larger for the field of medical image processing as the availability of a big data is not an easy coin to toss. To mitigate these obstacles, several supervised biomedical segmentation methods focus on manually-engineered preprocessing steps and architectures [2]}, [3]}. Manually-tuned data augmentation is also used to increase the number of training examples [4]}, [5]}.
